1. Field of the Invention
The present invention relates to a machine learning apparatus that learns laser machining condition data including driving power data supplied to a laser oscillator, a laser machining system, and a machine learning method.
2. Description of the Related Art
In the laser machining system that performs machining such as cutting or welding for a workpiece, the machining is preferably performed under an optimal laser machining condition so as to obtain machined processing results high in machining accuracy and machining quality at a high speed.
There are various techniques for searching for or learning machining conditions. For example, Japanese Unexamined Patent Publication (Kokai) No. 4-75108 discloses “an automatic machining condition determination apparatus including: an attribute data storage unit that stores data relating to a workpiece, data relating to a tool, and data relating to a machine; a neural network in which the data stored in the attribute data storage unit is an input and an internal constant in an arithmetic equation for determining a machining condition of a numerically controlled machine tool is an output; a machining condition arithmetic unit that obtains a machining condition based on the data stored in the attribute data storage unit and the internal constant; an internal constant correction unit that corrects, when the machining condition is corrected, the associated internal constant according to a correction value of the machining condition; and a weight correction unit that corrects a weight of the neural network so as to obtain the internal constant corrected by the internal constant correction unit with respect to the same input as that before the correction”.
Further, Japanese Unexamined Patent Publication (Kokai) No. 4-354653 discloses “a machining condition generation apparatus including: a machining condition generation unit; a machining condition characteristic data unit that stores characteristic data preferably used for generating the machining condition; and a learning unit that optimizes the machining condition based on a learning function, in which a changed data unit that stores a changed content of the machining condition is provided, the machining condition is changed according to the changed content, and the learning unit optimizes at least one of the machining condition generation unit and the machining condition characteristic data unit based on the changed content.”
Further, Japanese Unexamined Patent Publication (Kokai) No. 11-85210 discloses “a laser machining machine assistance apparatus including: an inference value generation unit that generates an inference value with respect to a machining condition parameter of laser machining by an artificial intelligence function; a display unit that displays the inference value generated by the inference value generation unit; and an input unit that inputs an evaluation parameter for evaluating a machining state, in which the inference value generation unit includes a machining condition parameter selection unit that selects a machining condition parameter most effective for correcting a current machining state.”
Further, Japanese Unexamined Patent Publication (Kokai) No. 2008-36812 discloses “a machining condition search apparatus including: an experimental machining condition generation unit; a machine that performs machining under a machining condition output from the experimental machining condition generation unit to output a real machined result; and a machining characteristic model unit that generates a machining characteristic model as an optimal machining condition when a predetermined machining condition is input, in which the experimental machining condition generation unit generates an experimental machining condition by using the machining characteristic model.”
Further, Japanese Unexamined Patent Publication (Kokai) No. 2012-236267 discloses “a machining condition search apparatus including: an experimental machining condition generation unit that generates an experimental machining condition by using a machining characteristic model showing a relationship between a machining condition and a machined result; a machined result collection unit that collects machined results of experimental machining according to the experimental machining condition generated by the experimental machining condition generation unit, and stores a set of the machined result and the experimental machining condition as experimental machining data; a first machining characteristic model generation unit that outputs a machining characteristic model newly generated by using the experimental machining data to the experimental machining condition generation unit; a second machining characteristic mode generation unit that generates, for each experimental machining data, while changing machining quality evaluations included in the machined results in the experimental machining data one by one, a new machining characteristic model reflecting the changed machining quality evaluations; a machining characteristic model synthesis unit that synthesizes the new machining characteristic model generated by the second machining characteristic model generation unit; and an optimal machining condition generation unit that generates an optimal machining condition from the machining characteristic model synthesized by the machining characteristic model synthesis unit.”